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Achieving High AI Accuracy on Data Tasks

· 2 min read
Team EasyManage

Achieving High AI Accuracy on Data Tasks​

EasyManage AI achieves high near-100% accuracy on the data tasks such as database querying, joining tables in a database, joining across data sources, database transactions.

  • This is possible with EasyManage innovation:
    • The separation of data query planning from execution
    • Enabling SQL-like calling via Tools and APIs
    • Data modeling, table joins to bring relevant data in one shot.
    • Database Transactions
    • Data mesh to consolidate data from multiple sources into one call.

We present to you how EasyManage AI's approach helps you succeed on data solution's enterprise AI reliability.

EasyManage AI Approach​

EasyManage AI's innovative approaches help towards

  • Avoiding the accuracy degradation that occurs when AI models need to fire multiple queries and process large amounts of data within context.
  • Reduce Context rot
  • By-pass maximum Tools limits for LLMs

A note on limitations: While EasyManage AI's approach is designed for efficient AI data analysis and automation. For purely generative tasks the accuracy will be what the underlying LLMs offer.

The separation of data query planning from execution​

  • We provision LLMs access to full business domain schema
  • LLMs are able to understand business domain schema very well,
    • Then with capability to construct SQL query operands and pass to Tools & API, they are able to accurately retrieve only required data, reducing data context size.

Flexible SQL-like calling via Tools and APIs​

EasyManage enables SQL-like calling via Tools and APIs, which LLMs use very nicely. Please navigate thru AI case studies to see examples.

Data Modeling​

  • Queries on table joins to bring relevant data from multiple tables in one shot.
  • Configure upto five-table joins via UI with type 1:M or 1:1 on each join.

Database Transactions​

  • You can easily implement AI transactions, allow LLMs to store data or Query results via database CRUD calls.
  • Incrementally store query-retrieved data to arrive at final consolidations.
  • Make available historical data context to LLMs

Data Mesh​

  • Data mesh to consolidate data from multiple data sources into one call.

Conclusion​

Get End Result → High AI Accuracy on Data Tasks